All Data clusters (Deep learning, DEL (Deep Embedding Clustering layer))

DEC_Embedding = read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/DEC_Embedding.csv')
head(DEC_Embedding)

resultft_DEL_all <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/resultft_DEL_all.csv')
# replacing the empty space "" values with no as done in the main analysis file
resultft_DEL_all$farmlive[resultft_DEL_all$farmlive == ""] <- NA
resultft_DEL_all <-  resultft_DEL_all %>% replace_na (list(farmlive = 'no'))
#tsne_converted_food$cl_DEL <- factor(resultft_DEL_all$cluster)
#ggplot(tsne_converted_food, aes(x=X, y=Y, color=cl_DEL)) + geom_point()
resultft_DEL_all$cluster <- as.factor(resultft_DEL_all$cluster)
dist_plot_clust <-function(original_data, selected_variable){
  selected_variable <- enquo(selected_variable)
  ggplot(original_data, aes(UQ(selected_variable))) + geom_density(aes(fill = factor(cluster)), alpha=0.8) +
    labs(title = "Density plot",
         subtitle="sIgE_f1 of persons Grouped by Clusters",
         caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
         x="sIgE_f1",
         fill="# Clusters")
} 
dist_plot_clust(original_data = resultft_DEL_all, selected_variable = age)

Density plot shoiwing the age distribution for each cluster

resultft_DEL_all$cluster <- as.factor(resultft_DEL_all$cluster)
age_g <- ggplot(resultft_DEL_all, aes(sIgE_f3))
age_p <- age_g + geom_density(aes(fill=factor(cluster)), alpha=0.8) +
    labs(title="Density plot",
         subtitle="sIgE_f1 of persons Grouped by Clusters",
         caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
         x="sIgE_f1",
         fill="# Clusters")
ggplotly(age_p)
g <- ggplot(resultft_DEL_all, aes(bmi2)) + scale_fill_brewer(palette = "Spectral")
s <- g + geom_histogram(aes(fill=factor(cluster)), 
                   bins=5, 
                   col="black", 
                   size=.1) +   # change number of bins
  labs(title="Histogram with Fixed Bins", 
       subtitle="Age across different clusters",
       x="Age",
         fill="# Clusters") 
ggplotly(s)
table_uft_DEL_all <- tableby(cluster ~ ., data = as.list(resultft_DEL_all))
summary(table_uft_DEL_all, title = "Charachtaristcs of Clusters")

Table: Charachtaristcs of Clusters
0 (N=526) 1 (N=328) 2 (N=910) 3 (N=108) Total (N=1872) p value
sIgE_f1 < 0.001
   Mean (SD) 0.030 (0.122) 0.068 (0.493) 0.000 (0.000) 1.076 (7.174) 0.082 (1.747)
   Range 0.000 - 1.060 0.000 - 8.179 0.000 - 0.000 0.000 - 73.692 0.000 - 73.692
sIgE_f2 < 0.001
   Mean (SD) 0.038 (0.138) 0.082 (0.364) 0.000 (0.000) 0.610 (2.148) 0.060 (0.558)
   Range 0.000 - 1.091 0.000 - 3.807 0.000 - 0.000 0.000 - 13.623 0.000 - 13.623
sIgE_f3 < 0.001
   Mean (SD) 0.004 (0.013) 0.008 (0.022) 0.000 (0.000) 0.113 (0.261) 0.009 (0.069)
   Range 0.000 - 0.080 0.000 - 0.137 0.000 - 0.000 0.000 - 1.332 0.000 - 1.332
sIgE_f4 < 0.001
   Mean (SD) 0.018 (0.095) 0.093 (0.379) 0.000 (0.000) 0.886 (1.944) 0.073 (0.534)
   Range 0.000 - 1.280 0.000 - 4.347 0.000 - 0.000 0.000 - 12.512 0.000 - 12.512
sIgE_f13 < 0.001
   Mean (SD) 0.030 (0.091) 0.220 (0.759) 0.000 (0.001) 4.809 (19.740) 0.324 (4.861)
   Range 0.000 - 0.927 0.000 - 11.866 0.000 - 0.020 0.000 - 149.746 0.000 - 149.746
sIgE_f14 < 0.001
   Mean (SD) 0.004 (0.019) 0.033 (0.125) 0.000 (0.000) 0.684 (1.847) 0.046 (0.472)
   Range 0.000 - 0.310 0.000 - 1.086 0.000 - 0.000 0.000 - 12.386 0.000 - 12.386
sIgE_f17 < 0.001
   Mean (SD) 0.312 (0.873) 2.484 (3.286) 0.000 (0.013) 22.025 (21.996) 1.794 (7.461)
   Range 0.000 - 11.197 0.000 - 13.628 0.000 - 0.398 0.000 - 111.259 0.000 - 111.259
sIgE_f18 < 0.001
   Mean (SD) 0.017 (0.168) 0.017 (0.039) 0.001 (0.025) 0.579 (4.510) 0.042 (1.091)
   Range 0.000 - 2.901 0.000 - 0.384 0.000 - 0.686 0.000 - 46.879 0.000 - 46.879
sIgE_f20 < 0.001
   Mean (SD) 0.011 (0.027) 0.094 (0.142) 0.000 (0.001) 1.106 (1.703) 0.083 (0.485)
   Range 0.000 - 0.193 0.000 - 0.915 0.000 - 0.015 0.000 - 9.959 0.000 - 9.959
sIgE_f36 < 0.001
   Mean (SD) 0.011 (0.025) 0.067 (0.129) 0.000 (0.002) 0.412 (1.090) 0.039 (0.283)
   Range 0.000 - 0.225 0.000 - 1.051 0.000 - 0.045 0.000 - 7.754 0.000 - 7.754
gender2 < 0.001
   females 329 (62.5%) 185 (56.4%) 453 (49.8%) 55 (50.9%) 1022 (54.6%)
   males 197 (37.5%) 143 (43.6%) 457 (50.2%) 53 (49.1%) 850 (45.4%)
age < 0.001
   Mean (SD) 47.196 (15.214) 48.255 (15.130) 51.199 (15.617) 41.367 (14.267) 48.991 (15.551)
   Range 18.146 - 76.877 19.058 - 78.075 18.875 - 77.746 19.415 - 77.130 18.146 - 78.075
bmi2 < 0.001
   Mean (SD) 27.698 (3.266) 30.328 (6.497) 24.411 (2.776) 26.762 (4.942) 26.507 (4.540)
   Range 18.904 - 34.816 18.290 - 50.058 16.975 - 31.556 17.915 - 38.955 16.975 - 50.058
farmlive
    0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%)
   no 460 (87.5%) 292 (89.0%) 789 (86.7%) 107 (99.1%) 1648 (88.0%)
   yes 66 (12.5%) 36 (11.0%) 121 (13.3%) 1 (0.9%) 224 (12.0%)
family_allergy_hist < 0.001
   no 229 (43.5%) 132 (40.2%) 633 (69.6%) 28 (25.9%) 1022 (54.6%)
   yes 297 (56.5%) 196 (59.8%) 277 (30.4%) 80 (74.1%) 850 (45.4%)
# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")

Charachtiristic Analysis

resultft_DEL_all$cluster <- as.factor(resultft_DEL_all$cluster)
catdes(resultft_DEL_all, 16)

Random Data clusters with DEL (Deep Embedding Clustering layer)

#result_rand_uft_DEL_k <- read.csv("/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_rand_food_uft_DEL_k.csv")
result_rand_uft_DEL_k <- read.csv("/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_rand_uft_DEL_k.csv")
result_rand_uft_DEL_k$farmlive[result_rand_uft_DEL_k$farmlive == ""] <- NA
result_rand_uft_DEL_k <-  result_rand_uft_DEL_k %>% replace_na (list(farmlive = 'no'))
table_rand_uft_DEL_k <- tableby(cluster ~ ., data = as.list(result_rand_uft_DEL_k))
summary(table_rand_uft_DEL_k, title = "Charachtaristcs of Clusters")

Table: Charachtaristcs of Clusters
0 (N=285) 1 (N=472) 2 (N=110) 3 (N=236) Total (N=1103) p value
sIgE_f1 < 0.001
   Mean (SD) 0.001 (0.009) 0.000 (0.000) 0.067 (0.291) 0.044 (0.149) 0.016 (0.117)
   Range 0.000 - 0.092 0.000 - 0.000 0.000 - 2.845 0.000 - 1.032 0.000 - 2.845
sIgE_f2 < 0.001
   Mean (SD) 0.009 (0.066) 0.000 (0.000) 0.186 (0.829) 0.042 (0.148) 0.030 (0.277)
   Range 0.000 - 0.640 0.000 - 0.000 0.000 - 6.849 0.000 - 1.091 0.000 - 6.849
sIgE_f3 < 0.001
   Mean (SD) 0.001 (0.006) 0.000 (0.000) 0.016 (0.042) 0.006 (0.017) 0.003 (0.016)
   Range 0.000 - 0.045 0.000 - 0.000 0.000 - 0.213 0.000 - 0.080 0.000 - 0.213
sIgE_f4 < 0.001
   Mean (SD) 0.005 (0.076) 0.000 (0.000) 0.238 (0.945) 0.048 (0.222) 0.035 (0.324)
   Range 0.000 - 1.280 0.000 - 0.000 0.000 - 8.417 0.000 - 2.543 0.000 - 8.417
sIgE_f13 0.002
   Mean (SD) 0.017 (0.077) 0.002 (0.030) 1.591 (12.755) 0.107 (0.312) 0.187 (4.042)
   Range 0.000 - 0.800 0.000 - 0.642 0.000 - 133.659 0.000 - 1.957 0.000 - 133.659
sIgE_f14 < 0.001
   Mean (SD) 0.000 (0.003) 0.000 (0.000) 0.203 (0.946) 0.020 (0.092) 0.025 (0.306)
   Range 0.000 - 0.040 0.000 - 0.000 0.000 - 8.505 0.000 - 0.833 0.000 - 8.505
sIgE_f17 < 0.001
   Mean (SD) 0.267 (0.781) 0.007 (0.066) 9.076 (15.899) 0.921 (2.133) 1.174 (5.760)
   Range 0.000 - 4.561 0.000 - 1.014 0.000 - 76.467 0.000 - 11.197 0.000 - 76.467
sIgE_f18 0.012
   Mean (SD) 0.005 (0.042) 0.001 (0.015) 0.477 (4.469) 0.026 (0.219) 0.055 (1.416)
   Range 0.000 - 0.686 0.000 - 0.322 0.000 - 46.879 0.000 - 2.901 0.000 - 46.879
sIgE_f20 < 0.001
   Mean (SD) 0.010 (0.034) 0.000 (0.004) 0.399 (1.057) 0.043 (0.117) 0.052 (0.357)
   Range 0.000 - 0.272 0.000 - 0.057 0.000 - 9.959 0.000 - 0.915 0.000 - 9.959
sIgE_f36 < 0.001
   Mean (SD) 0.009 (0.026) 0.001 (0.005) 0.163 (0.763) 0.036 (0.112) 0.027 (0.250)
   Range 0.000 - 0.235 0.000 - 0.067 0.000 - 7.754 0.000 - 1.051 0.000 - 7.754
gender2 0.229
   females 150 (52.6%) 263 (55.7%) 58 (52.7%) 112 (47.5%) 583 (52.9%)
   males 135 (47.4%) 209 (44.3%) 52 (47.3%) 124 (52.5%) 520 (47.1%)
age 0.001
   Mean (SD) 49.396 (14.935) 51.639 (15.931) 45.611 (14.833) 51.412 (14.636) 50.410 (15.390)
   Range 18.146 - 77.746 18.875 - 77.259 20.867 - 77.130 18.379 - 76.628 18.146 - 77.746
bmi2 < 0.001
   Mean (SD) 26.126 (2.338) 23.553 (2.460) 31.668 (6.216) 28.599 (3.067) 26.107 (4.127)
   Range 19.223 - 31.644 16.975 - 29.835 18.939 - 44.816 19.818 - 36.523 16.975 - 44.816
farmlive
    0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%)
   no 252 (88.4%) 382 (80.9%) 107 (97.3%) 219 (92.8%) 960 (87.0%)
   yes 33 (11.6%) 90 (19.1%) 3 (2.7%) 17 (7.2%) 143 (13.0%)
family_allergy_hist < 0.001
   no 164 (57.5%) 361 (76.5%) 38 (34.5%) 132 (55.9%) 695 (63.0%)
   yes 121 (42.5%) 111 (23.5%) 72 (65.5%) 104 (44.1%) 408 (37.0%)
# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")

Charachtiristic Analysis

result_rand_uft_DEL_k$cluster <- as.factor(result_rand_uft_DEL_k$cluster)
#result_food_uft_DEL_k <- result_food_uft_DEL_k[-c(1,2,20)]
catdes(result_rand_uft_DEL_k, 16)

With asthma and Rhinitis Data clusters with DEL (Deep Embedding Clustering layer)

result_as_rh_uft_DEL <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_as_rh_uft_DEL.csv')
result_as_rh_uft_DEL$farmlive[result_as_rh_uft_DEL$farmlive == ""] <- NA
result_as_rh_uft_DEL <-  result_as_rh_uft_DEL %>% replace_na (list(farmlive = 'no'))
table_as_rh_uft_DEL <- tableby(cluster ~ ., data = as.list(result_as_rh_uft_DEL))
summary(table_as_rh_uft_DEL, title = "Charachtaristcs of Clusters")

Table: Charachtaristcs of Clusters
0 (N=708) 1 (N=31) 2 (N=269) 3 (N=70) Total (N=1078) p value
sIgE_f1 < 0.001
   Mean (SD) 0.008 (0.070) 0.527 (1.012) 0.088 (0.546) 1.446 (8.885) 0.136 (2.300)
   Range 0.000 - 1.060 0.010 - 4.695 0.000 - 8.179 0.000 - 73.692 0.000 - 73.692
sIgE_f2 < 0.001
   Mean (SD) 0.004 (0.035) 2.043 (2.909) 0.056 (0.149) 0.293 (1.632) 0.094 (0.728)
   Range 0.000 - 0.551 0.034 - 13.006 0.000 - 0.850 0.000 - 13.623 0.000 - 13.623
sIgE_f3 < 0.001
   Mean (SD) 0.001 (0.006) 0.243 (0.361) 0.011 (0.024) 0.067 (0.194) 0.015 (0.090)
   Range 0.000 - 0.080 0.015 - 1.244 0.000 - 0.137 0.000 - 1.332 0.000 - 1.332
sIgE_f4 < 0.001
   Mean (SD) 0.002 (0.014) 0.538 (1.328) 0.107 (0.316) 1.063 (2.256) 0.112 (0.687)
   Range 0.000 - 0.224 0.030 - 7.445 0.000 - 2.275 0.000 - 12.512 0.000 - 12.512
sIgE_f13 < 0.001
   Mean (SD) 0.022 (0.094) 6.986 (25.028) 0.287 (0.859) 1.913 (4.129) 0.411 (4.497)
   Range 0.000 - 0.927 0.017 - 133.659 0.000 - 11.866 0.000 - 23.815 0.000 - 133.659
sIgE_f14 < 0.001
   Mean (SD) 0.001 (0.007) 0.430 (1.026) 0.052 (0.182) 0.704 (2.127) 0.071 (0.601)
   Range 0.000 - 0.160 0.000 - 4.468 0.000 - 1.475 0.000 - 12.386 0.000 - 12.386
sIgE_f17 < 0.001
   Mean (SD) 0.150 (0.435) 1.714 (3.833) 4.567 (5.110) 25.396 (26.534) 2.937 (9.526)
   Range 0.000 - 2.715 0.000 - 17.146 0.000 - 23.778 0.000 - 111.259 0.000 - 111.259
sIgE_f18 < 0.001
   Mean (SD) 0.006 (0.069) 0.068 (0.137) 0.042 (0.217) 0.818 (5.599) 0.069 (1.436)
   Range 0.000 - 1.678 0.000 - 0.585 0.000 - 2.901 0.000 - 46.879 0.000 - 46.879
sIgE_f20 < 0.001
   Mean (SD) 0.008 (0.029) 0.392 (0.776) 0.161 (0.277) 1.179 (2.058) 0.133 (0.625)
   Range 0.000 - 0.343 0.000 - 3.849 0.000 - 1.920 0.000 - 9.959 0.000 - 9.959
sIgE_f36 < 0.001
   Mean (SD) 0.009 (0.028) 0.100 (0.180) 0.082 (0.157) 0.487 (1.335) 0.061 (0.368)
   Range 0.000 - 0.277 0.000 - 0.845 0.000 - 1.051 0.000 - 7.754 0.000 - 7.754
gender2 0.354
   females 408 (57.6%) 16 (51.6%) 149 (55.4%) 33 (47.1%) 606 (56.2%)
   males 300 (42.4%) 15 (48.4%) 120 (44.6%) 37 (52.9%) 472 (43.8%)
age < 0.001
   Mean (SD) 49.324 (15.421) 48.398 (19.396) 44.784 (14.798) 40.863 (12.795) 47.615 (15.443)
   Range 19.266 - 76.656 19.415 - 77.130 19.058 - 76.190 20.741 - 78.075 19.058 - 78.075
bmi2 < 0.001
   Mean (SD) 25.960 (3.499) 28.986 (5.065) 27.843 (5.614) 30.970 (8.239) 26.842 (4.790)
   Range 17.404 - 34.484 19.044 - 38.514 17.915 - 40.083 20.381 - 50.058 17.404 - 50.058
farmlive
    0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%)
   no 637 (90.0%) 29 (93.5%) 238 (88.5%) 64 (91.4%) 968 (89.8%)
   yes 71 (10.0%) 2 (6.5%) 31 (11.5%) 6 (8.6%) 110 (10.2%)
family_allergy_hist < 0.001
   no 347 (49.0%) 8 (25.8%) 98 (36.4%) 22 (31.4%) 475 (44.1%)
   yes 361 (51.0%) 23 (74.2%) 171 (63.6%) 48 (68.6%) 603 (55.9%)
# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")

Charachtiristic Analysis

result_as_rh_uft_DEL$cluster <- as.factor(result_as_rh_uft_DEL$cluster)
catdes(result_as_rh_uft_DEL, 16)

Without asthma and Rhinitis Data clusters with DEL (Deep Embedding Clustering layer)

result_no_as_rh_uft_DEL <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_no_as_rh_uft_DEL.csv')
result_no_as_rh_uft_DEL$farmlive[result_no_as_rh_uft_DEL$farmlive == ""] <- NA
result_no_as_rh_uft_DEL <-  result_no_as_rh_uft_DEL %>% replace_na (list(farmlive = 'no'))
table_no_as_rh_uft_DEL <- tableby(cluster ~ ., data = as.list(result_no_as_rh_uft_DEL))
summary(table_no_as_rh_uft_DEL, title = "Charachtaristcs of Clusters")

Table: Charachtaristcs of Clusters
0 (N=134) 1 (N=14) 2 (N=55) 3 (N=591) Total (N=794) p value
sIgE_f1 < 0.001
   Mean (SD) 0.053 (0.148) 0.017 (0.045) 0.005 (0.019) 0.000 (0.000) 0.010 (0.064)
   Range 0.000 - 0.918 0.000 - 0.149 0.000 - 0.098 0.000 - 0.000 0.000 - 0.918
sIgE_f2 < 0.001
   Mean (SD) 0.059 (0.166) 0.055 (0.156) 0.046 (0.290) 0.000 (0.000) 0.014 (0.106)
   Range 0.000 - 1.091 0.000 - 0.559 0.000 - 2.143 0.000 - 0.000 0.000 - 2.143
sIgE_f3 < 0.001
   Mean (SD) 0.008 (0.018) 0.017 (0.043) 0.004 (0.014) 0.000 (0.000) 0.002 (0.011)
   Range 0.000 - 0.067 0.000 - 0.134 0.000 - 0.065 0.000 - 0.000 0.000 - 0.134
sIgE_f4 < 0.001
   Mean (SD) 0.043 (0.148) 0.254 (0.653) 0.097 (0.462) 0.000 (0.000) 0.019 (0.164)
   Range 0.000 - 1.129 0.000 - 2.031 0.000 - 2.543 0.000 - 0.000 0.000 - 2.543
sIgE_f13 < 0.001
   Mean (SD) 0.046 (0.165) 10.907 (39.964) 0.099 (0.280) 0.000 (0.000) 0.207 (5.315)
   Range 0.000 - 1.596 0.000 - 149.746 0.000 - 1.486 0.000 - 0.000 0.000 - 149.746
sIgE_f14 < 0.001
   Mean (SD) 0.011 (0.047) 0.456 (1.336) 0.028 (0.122) 0.000 (0.000) 0.012 (0.185)
   Range 0.000 - 0.366 0.000 - 4.886 0.000 - 0.728 0.000 - 0.000 0.000 - 4.886
sIgE_f17 < 0.001
   Mean (SD) 0.408 (1.130) 4.284 (10.816) 1.412 (4.447) 0.000 (0.000) 0.242 (1.977)
   Range 0.000 - 7.065 0.000 - 36.193 0.000 - 19.736 0.000 - 0.000 0.000 - 36.193
sIgE_f18 < 0.001
   Mean (SD) 0.007 (0.032) 0.096 (0.248) 0.016 (0.050) 0.000 (0.000) 0.004 (0.039)
   Range 0.000 - 0.322 0.000 - 0.868 0.000 - 0.261 0.000 - 0.000 0.000 - 0.868
sIgE_f20 < 0.001
   Mean (SD) 0.025 (0.073) 0.366 (0.762) 0.067 (0.171) 0.000 (0.000) 0.015 (0.122)
   Range 0.000 - 0.520 0.000 - 2.149 0.000 - 0.849 0.000 - 0.000 0.000 - 2.149
sIgE_f36 < 0.001
   Mean (SD) 0.021 (0.060) 0.140 (0.315) 0.038 (0.121) 0.000 (0.000) 0.009 (0.061)
   Range 0.000 - 0.366 0.000 - 1.081 0.000 - 0.734 0.000 - 0.000 0.000 - 1.081
gender2 0.039
   females 57 (42.5%) 6 (42.9%) 26 (47.3%) 327 (55.3%) 416 (52.4%)
   males 77 (57.5%) 8 (57.1%) 29 (52.7%) 264 (44.7%) 378 (47.6%)
age 0.472
   Mean (SD) 50.340 (15.466) 50.315 (14.955) 47.934 (14.331) 51.261 (15.644) 50.858 (15.512)
   Range 18.379 - 76.877 22.091 - 74.242 19.428 - 74.099 18.146 - 77.746 18.146 - 77.746
bmi2 < 0.001
   Mean (SD) 28.253 (3.789) 37.507 (7.834) 32.104 (3.784) 24.719 (2.772) 26.053 (4.136)
   Range 18.904 - 37.109 22.097 - 46.094 23.356 - 40.164 16.975 - 32.076 16.975 - 46.094
farmlive
    0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%)
   no 112 (83.6%) 13 (92.9%) 48 (87.3%) 507 (85.8%) 680 (85.6%)
   yes 22 (16.4%) 1 (7.1%) 7 (12.7%) 84 (14.2%) 114 (14.4%)
family_allergy_hist 0.978
   no 93 (69.4%) 10 (71.4%) 39 (70.9%) 405 (68.5%) 547 (68.9%)
   yes 41 (30.6%) 4 (28.6%) 16 (29.1%) 186 (31.5%) 247 (31.1%)
# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")

Charachtiristic Analysis

result_no_as_rh_uft_DEL$cluster <- as.factor(result_no_as_rh_uft_DEL$cluster)
catdes(result_no_as_rh_uft_DEL, 16)

adding the id varaible

resultft_DEL_all$ID <- food_data_id$ID
result_rand_uft_DEL_k$ID <- rand_food_data_id$ID
result_as_rh_uft_DEL$ID <- as_ri_food_id$ID
result_no_as_rh_uft_DEL$ID <- no_as_ri_food_id$ID
write.csv(resultft_DEL_all,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/resultft_DEL_all_id.csv')
write.csv(result_rand_uft_DEL_k,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_rand_uft_DEL_k_id.csv')
write.csv(result_as_rh_uft_DEL,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_as_rh_uft_DEL_id.csv')
write.csv(result_no_as_rh_uft_DEL,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_no_as_rh_uft_DEL_id.csv')
---
title: "Results food data clustering DEL"
output:
  html_notebook: default
  pdf_document: default
---

```{r loadlib, include=FALSE}
library(FactoMineR)
library(factoextra)
library(arsenal)
library(Rtsne)
library(plotly)
library(tidyverse)
```

# All Data clusters (Deep learning, DEL (Deep Embedding Clustering layer))

```{r}
DEC_Embedding = read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/DEC_Embedding.csv')
head(DEC_Embedding)
```

```{r}
set.seed(10)
#tsne_converted_food_DEL <- Rtsne(X = EDL_Embedding ,perplexity= 200, is_distance = FALSE, check_duplicates = FALSE)
tsne_converted_food_DEC <- Rtsne(X = DEC_Embedding ,perplexity= 150, is_distance = FALSE, check_duplicates = FALSE)

tsne_converted_food_DEC <- tsne_converted_food_DEC$Y %>%
  data.frame() %>%
  setNames(c("X", "Y"))

tsne_converted_food_DEC$cl <- factor(resultft_DEL_all$cluster)
ggplot(tsne_converted_food_DEC, aes(x=X, y=Y, color=cl)) + geom_point()

#ggplot(aes(x = X, y = Y), data = tsne_converted_food_DEC)  + geom_point()
```

```{r}
tsne_converted_food_DEC_3d <- Rtsne(X = DEC_Embedding ,perplexity= 150, dims = 3, is_distance = FALSE, check_duplicates = FALSE)

tsne_converted_food_DEC_3d <- tsne_converted_food_DEC_3d$Y %>%
  data.frame() %>%
  setNames(c("X", "Y", "Z"))

tsne_converted_food_DEC_3d$cl <- factor(resultft_DEL_all$cluster)

p <- plot_ly(tsne_converted_food_DEC_3d, x = ~X, y = ~Y, z = ~Z, color = ~cl, colors = c('#BF382A', '#0C4B8E')) %>%
  add_markers() %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim2'),
                     zaxis = list(title = 'Dim3')))
p
```

```{r}
resultft_DEL_all <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/resultft_DEL_all.csv')
# replacing the empty space "" values with no as done in the main analysis file
resultft_DEL_all$farmlive[resultft_DEL_all$farmlive == ""] <- NA
resultft_DEL_all <-  resultft_DEL_all %>% replace_na (list(farmlive = 'no'))
#tsne_converted_food$cl_DEL <- factor(resultft_DEL_all$cluster)
#ggplot(tsne_converted_food, aes(x=X, y=Y, color=cl_DEL)) + geom_point()
resultft_DEL_all$cluster <- as.factor(resultft_DEL_all$cluster)
```

```{r}
dist_plot_clust <-function(original_data, selected_variable){
  selected_variable <- enquo(selected_variable)
  ggplot(original_data, aes(UQ(selected_variable))) + geom_density(aes(fill = factor(cluster)), alpha=0.8) +
    labs(title = "Density plot",
         subtitle="sIgE_f1 of persons Grouped by Clusters",
         caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
         x="sIgE_f1",
         fill="# Clusters")
} 
```

```{r}
dist_plot_clust(original_data = resultft_DEL_all, selected_variable = age)
```


### Density plot shoiwing the age distribution for each cluster
```{r}
resultft_DEL_all$cluster <- as.factor(resultft_DEL_all$cluster)

age_g <- ggplot(resultft_DEL_all, aes(sIgE_f3))
age_p <- age_g + geom_density(aes(fill=factor(cluster)), alpha=0.8) +
    labs(title="Density plot",
         subtitle="sIgE_f1 of persons Grouped by Clusters",
         caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
         x="sIgE_f1",
         fill="# Clusters")

ggplotly(age_p)
```


```{r}
g <- ggplot(resultft_DEL_all, aes(bmi2)) + scale_fill_brewer(palette = "Spectral")
s <- g + geom_histogram(aes(fill=factor(cluster)), 
                   bins=5, 
                   col="black", 
                   size=.1) +   # change number of bins
  labs(title="Histogram with Fixed Bins", 
       subtitle="Age across different clusters",
       x="Age",
         fill="# Clusters") 

ggplotly(s)
```

```{r}
table_uft_DEL_all <- tableby(cluster ~ ., data = as.list(resultft_DEL_all))
summary(table_uft_DEL_all, title = "Charachtaristcs of Clusters")
# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
```



## Charachtiristic Analysis
```{r, warning=FALSE}
resultft_DEL_all$cluster <- as.factor(resultft_DEL_all$cluster)
catdes(resultft_DEL_all, 16)
```



# Random Data clusters with DEL (Deep Embedding Clustering layer)

```{r}
#result_rand_uft_DEL_k <- read.csv("/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_rand_food_uft_DEL_k.csv")
result_rand_uft_DEL_k <- read.csv("/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_rand_uft_DEL_k.csv")
result_rand_uft_DEL_k$farmlive[result_rand_uft_DEL_k$farmlive == ""] <- NA
result_rand_uft_DEL_k <-  result_rand_uft_DEL_k %>% replace_na (list(farmlive = 'no'))
table_rand_uft_DEL_k <- tableby(cluster ~ ., data = as.list(result_rand_uft_DEL_k))
summary(table_rand_uft_DEL_k, title = "Charachtaristcs of Clusters")
# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
```

## Charachtiristic Analysis
```{r, warning=FALSE}
result_rand_uft_DEL_k$cluster <- as.factor(result_rand_uft_DEL_k$cluster)
#result_food_uft_DEL_k <- result_food_uft_DEL_k[-c(1,2,20)]
catdes(result_rand_uft_DEL_k, 16)
```

# With asthma and Rhinitis Data clusters with DEL (Deep Embedding Clustering layer)

```{r}
result_as_rh_uft_DEL <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_as_rh_uft_DEL.csv')
result_as_rh_uft_DEL$farmlive[result_as_rh_uft_DEL$farmlive == ""] <- NA
result_as_rh_uft_DEL <-  result_as_rh_uft_DEL %>% replace_na (list(farmlive = 'no'))
table_as_rh_uft_DEL <- tableby(cluster ~ ., data = as.list(result_as_rh_uft_DEL))
summary(table_as_rh_uft_DEL, title = "Charachtaristcs of Clusters")
# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
```

## Charachtiristic Analysis
```{r, warning=FALSE}
result_as_rh_uft_DEL$cluster <- as.factor(result_as_rh_uft_DEL$cluster)
catdes(result_as_rh_uft_DEL, 16)
```


# Without asthma and Rhinitis Data clusters with DEL (Deep Embedding Clustering layer)

```{r}
result_no_as_rh_uft_DEL <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_no_as_rh_uft_DEL.csv')
result_no_as_rh_uft_DEL$farmlive[result_no_as_rh_uft_DEL$farmlive == ""] <- NA
result_no_as_rh_uft_DEL <-  result_no_as_rh_uft_DEL %>% replace_na (list(farmlive = 'no'))
table_no_as_rh_uft_DEL <- tableby(cluster ~ ., data = as.list(result_no_as_rh_uft_DEL))
summary(table_no_as_rh_uft_DEL, title = "Charachtaristcs of Clusters")
# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
```

## Charachtiristic Analysis
```{r, warning=FALSE}
result_no_as_rh_uft_DEL$cluster <- as.factor(result_no_as_rh_uft_DEL$cluster)
catdes(result_no_as_rh_uft_DEL, 16)
```




# adding the id varaible
```{r}
resultft_DEL_all$ID <- food_data_id$ID
result_rand_uft_DEL_k$ID <- rand_food_data_id$ID
result_as_rh_uft_DEL$ID <- as_ri_food_id$ID
result_no_as_rh_uft_DEL$ID <- no_as_ri_food_id$ID
write.csv(resultft_DEL_all,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/resultft_DEL_all_id.csv')
write.csv(result_rand_uft_DEL_k,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_rand_uft_DEL_k_id.csv')
write.csv(result_as_rh_uft_DEL,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_as_rh_uft_DEL_id.csv')
write.csv(result_no_as_rh_uft_DEL,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_no_as_rh_uft_DEL_id.csv')
```



















